Title

Should We Discard Sparse Or Incomplete Videos?

Keywords

Action classification; semi-supervised learning; sparse video; tensor decomposition

Abstract

In this paper, we determine whether incomplete videos that are often discarded carry useful information for action recognition, and if so, how one can represent such mixed collection of video data (complete versus incomplete, and labeled versus unlabeled) in a unified manner. We propose a novel framework to handle incomplete videos in action classification, and make three main contributions: (1) We cast the action classification problem for a mixture of complete and incomplete data as a semi-supervised learning problem of labeled and unlabeled data. (2) We introduce a two-step approach to convert the input mixed data into a uniform compact representation. (3) Exhaustively scrutinizing 280 configurations, we experimentally show on our two created benchmarks that, even the videos are extremely sparse and incomplete, it is still possible to recover useful information from them, and classify unknown actions by a graph based semi-supervised learning framework.

Publication Date

1-28-2014

Publication Title

2014 IEEE International Conference on Image Processing, ICIP 2014

Number of Pages

2502-2506

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICIP.2014.7025506

Socpus ID

84949927273 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84949927273

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